Cross-Validation Techniques

Explore cross-validation techniques to reliably evaluate machine learning models. Understand k-fold and stratified k-fold validation for regression and classification, avoid data leakage with proper pipelines, and learn when to use time-aware or leave-one-out methods. Gain skills to assess model stability and ensure consistent performance across different data splits.

We'll cover the following...

K-fold validation for energy usage
Stratification for rare fraud labels
When random folds are invalid

A single validation split can make the same model look strong on one random split and weak on another. The split may be lucky because its validation rows are easy, or unlucky because they contain unusually difficult cases. Rather than trust one partition, we need an estimate that uses the available training data more completely while preserving the rule that the final held-out test set remains untouched.

K-fold cross-validation divides the available development data into $k$ parts, then repeats fitting and validation $k$ times. A fold is one held-out partition. In each run, the model trains on $k-1$ ...

1.Get Started

2.SQL for Data Analysis

3.From SQL to Python: Working with Data in NumPy and pandas

4.Statistics for Data Science

5.Data Cleaning and Exploratory Analysis

6.Feature Engineering

7.Data Visualization and Storytelling

8.Framing a Responsible ML Problem

9.Linear Regression and Model Evaluation

10.Classification Models

11.Generalization, Validation, and Regularization

12.Decision Trees

13.Classification Evaluation

14.Bagging and Random Forests

15.Gradient Boosting

16.Model Interpretability and Deployment

17.Unsupervised Learning: Clustering and Dimensionality Reduction

18.Time Series Fundamentals

19.Forecasting Models and Evaluation

20.Text Analytics

21.A/B Testing and Experimentation

22.Recommender Systems

23.Wrapping Up

Cross-Validation Techniques